This finding can be explained by the quality of the input data: the URLs of the homepages have been screened manually before download, and the retrieved data seem to confirm that we nearly exclusively deal with explanatory contexts.

we can confirm that the selection of our corpus is optimized for the task at hand since it contains a large number of definitions by its construction principle.


Thus, we analyze a broad spectrum of Spanish linguistic phenomena to come up with suggestions to adapt the current guidelines so that it is possible to annotate AMRs for Spanish.

of this work, we make available the first public online repository containing manually annotated Spanish AMRs.


AMR as a semantic representation language appears based on the assumption that we lack a simple readable semantic bank of natural language sentences "paired with their whole-sentence, logical meanings" (Banarescu et al., 2013).

they started annotating the logical meaning of sentences, for which a particular sentence is encoded as a Directed Acyclic Graph (DAG).


As was mentioned above, we focus on lexical relations (paradigmatic and syntagmatic) between terms associated with a specialized field of knowledge.

our resource can complement others that represent mainly relations between lexical units (BabelNet or the FLN) or relations between specialized concepts (Envo and EcoLexicon).


Also, some of these inventories and their accompanying dataset are created from another application's perspective, and not for the sake of creating a noun compound dataset.

they cannot be used for learning noun compound interpretation in a general-purpose setting.


• We intended to create a dataset that can be used as a gold standard for further research in noun compound interpretation.

we tried to reduce false positives as much as possible, i.e., ensure that a noun compound included in the dataset is labeled with correct frame and frame element.


However, our restrictions led to the inclusion of only six noun compounds.

there is a scope for including many more noun compounds.


It usually takes him 10 minutes to bike to school.

in our annotation scheme, we make the distinction between time expressions that can be used as reference times and the ones that cannot.


Absolute concrete time expressions can be normalized independently and don't need a reference time.

we stipulate that their parent in the dependent tree is the pre-defined node ROOT.


The subtitled audio/video recording overcomes this discrepancy thus fulfilling the essential demand to analyze not only the transcript but the complete oral source.

audio Mining allows to take full advantage of the untapped potential of Oral History leading back to its original roots.


In this work we utilize the latter approach and try to train such a robust acoustic model.

we virtual corrupt the GerTV1000h corpus with data augmentation as described in subsection 5.4.


As described before, recordings in the broadcast-domain can be considered very clean.

for evaluation we mostly used the clean subset of the DiSCo corpus in our previous work.


In our multi-condition experiments we want to analyze the influence of training acoustic models using different mixtures of the Clean, Reverb, R+AWGN and R+RealNoise data-sets.

the data-sets we actually use for training are created by randomly selecting each file from one of the four different data-sets using one of the distributions set out in table 3.


However, the hyperparameter g is sensitive and only yields high performance in the range (0.6, 0.9).

the learnable gating mechanism could also be useful in some scenarios, as it is self-adaptive.


When predicting, scores of samples of each category is computed and the class of the highest score is chosen as its label.

feature dependence is taken into account in MaxEnt.


On the contrast, the accuracy increases slightly without the word unigrams, which reveals that word level features are helpless and even undermine the performance of language identification on the data set.

we stop using this feature in the following experiments.


In the DSL shared task, average accuracy (Acc) and macroaveraged F1-score (F1) were used as the official scores.

we use the same metrics in this experiment.


These annotations are the concatenation of the matching forward and backward hidden layer states obtained respectively by the forward and the backward RNN comprising the bidirectional RNN.

they contain the summaries of the dialogue turn contexts respectively preceding and following a considered word.


While it is still possible to use both the forced alignment and voice building plugins in the same Gradle project, a more efficient workflow is to build a data artifact, which is then available as a dependency for the proper voice building process.

this stage can be skipped if a corpus of speech data is already available with appropriate orthographic and phonetic annotations.


Refactoring the core system and of the voice building process has allowed us to separate the source code management (SCM) for each language and each voice project.

each language and voice can have its own SCM repository hosted on GitHub, 8 while the released artifacts are published to Bintray 9 and indexed in JCenter.


Using the T O metric, a hierarchy H 1 with edges would yield a score of T O(H 1 , H 2 ) = 1 (a perfect match), which contradicts our notion of a hierarchy branching from general to specific information.

we propose our new modification called the hierarchy overlap which is the weighted sum of T O, the superset overlap SupO, and the subset overlap SubO score.


The semantic cotopy of nugget X in H 2 is exactly the same set.

the taxonomy overlap of nugget X in hierarchies H 1 and H 2 equals The intersection of the respective supersets consists of only one nugget {A}, the union has four nuggets {A, B, D, E}.


This shows that the initial position within the result space does influence the optimization result, but the effects are small.

we run each optimization with ten different random seeds and use the result with the highest 1 3 3 i=1 HO(H G , H i ) as the gold standard.


However, this system analyzes neither the grammaticality nor the most used n-grams in the clusters.

the baseline system generated compressions with the worst ROUGE scores.


Since the judgment of annotators is objective, therefore we kept the selected tweets from the two annotators as the refer-ences.

the outputs of each extractive method have to compare to two references.


Data observation Unlike other types of data, tweets are created to share human thoughts and emotions under a length constraint.

people tend to put emotional words and hashtags in their tweets.


For the summarization tasks, summarization systems need to recognize significant content from each utterance to cover all important information in the conversation.

corpora are very important to analyze characteristics of conversations and to construct a method for summary generation.


eral researchers to the Kyutech corpus because there has been a lot of works on extractive techniques in conversation summarization (Murray et al., 2005, Hirohata et al., 2006.

construction of extractive summaries is effective and leads to a deeper analysis of the Kyutech corpus.


In multi-party conversation, extractive summarization is a difficult challenging task because the meeting transcripts are composed informal and disfluency utterances with overlapping speakers (McKeown et al., 2005).

extractive approaches for conversation summarization often differ from techniques of other domains, such as document summarization.


In other words, extractive summarization has an important role in summary generation.

we evaluate the extractive summarization task as the first step for our abstractive summarization.


The current models tended to extract important utterances from the beginning and the ending parts in conversations.

we compared the F-measure scores in each segment in conversations.


The beginning part of the conversation usually contains a trigger of the discussion.

the higher accuracy in the segment shows potential effectiveness of the models.


We annotated importance utterances that already exist in the Kyutech corpus, on the basis of reference summaries.

we linked 23 percent of utterances in conversations with sentences in reference summaries as extractive summaries.


recent studies have employed larger datasets, mostly based on Cable News Network (CNN), Daily Mail and Gigaword documents.


Summarization models are usually evaluated with noisy automatic evaluation systems (Lin, 2004).

the evaluation results might be inaccurate if the summarization models are only evaluated on a few topic.


it appears in every ED of the respective paraphrase set (use shovel for making a hole and use shovel for hole).


In particular, annotators had difficulties with annotating Inference cases.

we decided not to adjudicate the annotation, but average over the distributions for evaluation.


The current version of CEFR-LS is mainly intended as a pilot for constructing a language resource.

there remains room for future development.


As described in Sect.2, the service interface class translate has a list of corresponding atomic/composite service types (BackTranslation, MultihopTranslation, Translation, Translation-WithTemporalDictionary) defined by the Language Grid Ontology.

the API example in Table 2 can be used to invoke either an atomic translation service or a com-posite translation service, depending on the specified service endpoint URL.


This establishes a solid basis for the inclusiveness of minorities and people with special needs.

in a wider context multilingualism helps remove barriers, fosters collaboration and creates more cultural awareness.


7, pp156-160) identifies four major uses: (i) commenting on and justifying the title of the work; (ii) commenting on the text of the work (the most canonical usage); (iii) claiming a relationship to the cited author (name dropping); (iv) signaling the genre of the work (romantic and gothic authors use more epigraphs, classical and realistic fewer).

s authors use them both to set the theme and to link their work to the existing body of literature.


However, as the sampling experiments show, the performance of the attribution methods varies considerably with corpus composition.

only future research comparing the spread of the measures based on many samples across languages will be able to answer the question whether the variance between languages is mainly a result of the corpus setup or whether there is also a factor in play related to language typology.


The phenomenon of attribution has not typically been studied directly, but instead as part of other phenomena such as opinion analysis, discourse analysis, or the analysis of dialogue in narrative.

there are many existing resources that contain annotations relevant to attribution but which fail to fully capture all attributive phenomena or to adequately label all parts of attributions.


Furthermore, the target group has to be taken into account.

translating the English topics into German results into mismatch due to different culture-related controversies.


In Argotario, players have to first learn to recognize existing fallacies, before they are asked to write new ones.

the initial set of arguments is important for the first impression of the game to a new user.


Creating such data by hiring professional translators would be expensive, especially considering that it would have to be done for eleven target languages.

we turn our attention to crowdsourcing as a cost-saving alternative.


That is, the embeddings were trained to capture distributional similarity, not relative magnitude of impact.

this information, when present, is indirect and very likely noisy.


Unlike MSA, DA has a small number of printed bilingual or monolingual dictionaries.

building a DA lexicon with varying degrees of coverage and linguistic complexity has been the aim of several research efforts.


The trained model results in an embedding space, where NL patterns and KB predicates are represented as vectors in this space.

the similarity between an NL pattern and a KB predicate can be measured by their cosine similarity.


Formally, given an NL triple ( , , ) , ∈ ( ), ∈ ( ) , we have to predict a KB predicate such that ( , , ) is the corresponding fact in KB test set.

we generate NL test triples Λ and ground truth Λ as follows.


(2015) cannot be directly compared with ours under this task.

we built a baseline model through a counting-based approach that counts the co-occurrence between each pattern and each predicate found in the same or alias entity pairs.


For example, the pattern "is a fantasy novel by" infers the predicate "author".


Indeed, a common hypothesis is that larger window emphasizes the learning of domain similarity between words, while a narrow context window leads to hyponymic gathering.

a smaller size of context window could be more preferable in our task.


We assume situations where foreign visitors are speaking with local Japanese people because the goal of the corpus is to realize speech translation in such situations.

the corpus primarily consists of dialogues.


Another interesting aspect of Scielo is that several journals publish full-text of scientific articles in more than one language, a feature commonly limited to the abstracts.

the Scielo database can be a valuable source for parallel corpora for various scientific domains.


To carry out our experiments, we needed documents in Persian and English whose topics were the same.

we downloaded the document aligned Persian-English Wikipedia from Linguatools 3 .


Since it is an XML file, it contains markup language, links, tables, figures, and so forth.

all the unnecessary characters in the file need to be removed first.


In addition, we penalize the sentences that have more or less number of words than their translated equivalents.

the Penalty variable, which is the difference in the word number of the two sentences, makes the similarity score smaller when the difference is too large.


While different techniques have been proposed to learn such representations (Collobert and Weston, 2008;Mikolov et al., 2013b;Pennington et al., 2014), all rely on the distributional hypothesis -the idea that the meaning of a word is captured by the contexts in which it appears.

the quality of word vectors directly depends on the amount and quality of data they were trained on.


We restrict the vocabulary for the analogy tasks to the 200,000 most frequent words from the training data.

the models trained on Wikipedia and Wikipedia+Crawl do not share the exact same vocabulary (see coverage in Table 5).


Before splitting the data into train, dev and test sets, we theorized that having too similar documents in the train and the test sets could lead to a skewed (too optimistic) evaluation of any supervised summarization methods.

we wanted the documents that are close to each other in some sense to be put into the same part of the split.


The rest of the data was then clustered again into 5000 clusters by K-Means algorithm, again based on L2 similarity of their abstracts.

the clusters were randomly divided in rougly 86.5:4.5:4.5 ratio to form the standard train/dev/test split.


Also, for the purpose of estimating stable word distributions, it was decided that works had to be at least 150 kilobytes in length thus discarding authors with multiple shorter works.

there might be a bias towards more prominent writers, as there could be more incentive to make their data publicly available.


Thereby, a set of features is selected based on its accuracy in predicting the publication year of a text.

the prediction of a variable y using explanatory models is based on a function over a set of distinct variables: {x 1 , x 2 , .


However, the average training and test set error of 9.7 and 9.9 are extremely close to the RMSE scores of Douglas (10.0), Adams (12.0), Howells (11.8), Crawford (11.9) and Saltus (11.6), especially taking into account that their data would be truly unseen.

without also having examined combined models of or with other authors, it is not clear how close James and Twain are in terms of stylistic change regarding the features examined here.


The frequencies in the Dundee Corpus are not very representative of general word-frequencies of the English language, because of the small size of the corpus.

we suggest that the effect is driven rather by the presence of the antecedent in a particular text.


Given this sentence, we can infer that she began her career as a journalist in Tokyo, but it is not a trivial task to identify this implicit relation.

although the target language of our current work is Japanese, it has relevance to other types of languages as well.


The idea is to compute the most frequent tag of a token in a large NER annotated resource.

in case of ties during the annotation projection the most frequent entity tag will be assigned.


Given a large corpus annotated with Named Entities, we calculate the most frequent tag for each token in the corpus.

whenever we need to back-off, we simply consult the frequencies table obtained from Wikiner for the candidate token and assign to that token the most frequent one.


These clustering features replaced the features used in the original, gold-standard trained models.

the difference between the gold-standard and the projected models mentioned in Tables 5, 6    strict match projections.


(2001) use bridging between two languages to perform lemmatization in a third target language, and Fossum and Abney (2005) train multiple POS taggers from monolingual source data and combine their annotations to project them to a given target language.

to our knowledge, no previous approach aims at doing transfer of semantic annotations as we propose in this paper.


However, the class distributions vary significantly across all the thirteen languages (see Table 1).

using random samples to extract evaluation corpora may lead to very imbalanced test sets, i.e.


This type of training is not a cross-lingual approach any more.

we will refer to this method as "joint multilingual document classification".


Our work differs from previous related works in two ways: (1) we propose a method of RE at the paragraph-level-i.e., from a collection of multiple sentences-rather than extracting information from an independent single sentence; (2) our approach, which builds upon the sentence embedding, is more effective for language-independent extraction because it avoids high-level natural language processing (NLP) tools.

the present approach can be generally used for RE, even in languages for which NLP tools are lacking.


In everyday communication, people assume a shared common ground which forms a basis for efficiently resolving ambiguities and for inferring implicit information.

recoverable information is often left unmentioned or underspecified.


Due to annotation mistakes, we found a small number of chosen correct and incorrect answers to be inappropriate, that is, some "correct" answers were actually incorrect and vice versa.

we manually validated the complete dataset in a final step.


Part-Of-Speech Tagging (pos) Most loanwords are nouns.

we use part-ofspeech information as one of the additional feature to constraint the output of our model.


The final system has to meet the following requirements: R1) Examples with sentences from several languages, i. e. English, German and Turkish, are part of the dataset.

the system is supposed to be languageindependent instead of -specific.


Furthermore, RBG does not require language specific knowledge like hand-written grammars.

it fulfills the requirement of languageindependence, i. e. R1).


Originally, there is a jwcdg grammar available for German only.

jwcdg does not fulfill the languageindependence requirement.


In the example of Figures 1a and 3a, both paints and bed can be grounded, the former referring to the verb of the relative clause, which is in active voice.

the relative clause is supposed to be attached to bed.


In addition, it is based on RBG, which is a parser that enables state-of-the-art results.

all  Table 2: Comparison of the evaluation results of the original RBG and our multi-modal disambiguation regarding the ambiguous attachment of the relative clauses for the ambiguities A1) -A3) in different configurations (language, voice of the finite verb of the relative clause).


Our experiments show that the automatically calculated CO2-value estimates lie within an acceptable range compared to the manually calculated values.

the costs of the calculation of the CO2-footprints can be reduced dramatically by using the automatic approaches.


Section 3.2. describes the recipe matching approach, which estimates the CO2-value of a recipe based on similar recipes rather than the individual ingredients.

it does not depend on the completeness of the food product database.


Unlike the ingredient matching, the recipe matching does not rely on assigning the individual ingredients to a database entry.

this nearest neighbor approach overcomes the long tail problem introduced by the incompleteness of the ingredient database.


a recipe with 500g flour and 3g salt would be similar to a recipe with 500g salt and 3g flour.

we suggest a method that also considers the amounts as an additional information, so that recipes have a higher similarity if the difference between the amounts of their respective ingredient descriptions is small.


Hereby, we choose the formula so that a difference of zero leads to a weight of one.

we also store the amounts of each term in each recipe, so that we can quickly retrieve the amount of a term in a given recipe when comparing recipes.


However, not only the number of ingredients is different but also the ingredients themselves.

the most similar recipe from which the CO2value is used as an estimate is most likely a recipe from the same category (hobby or catering) as the input recipe.


This is likely due to the fact that our pivot model is based on newspaper data.

typical 'internet-speak' with jargon and contracted forms, such as 'ワロタ' or 'mjd', are frequently observed but mark unsurprisingly a low score, on par with the level of distinctly dialectal utterances.


The process of hiring human participants to identify dialects is very costly and it is a time consuming job.

machine automation could work as a quick and cheap alternative provided we can create an effective mix of new methods with the appropriate dataset.


which it can be confused by speakers of dialects which do not have the phoneme [p].

p Q is included in CAPHI as /p./ as it is useful in describing the dialectal differences between Iraqi and other dialects.


Arabic dialects differ greatly from MSA on the phonological, orthographic, morphological and lexical levels.

to determine which dialect should be processed in a text, it is unavoidable to use criteria related to these levels of analysis.


More importantly, the dataset size we want to annotate is quite large, hence it is too expensive to recruit in-house annotators for that task.

we used CrowdFlower crowdsourcing platform 9 to acquire   annotations.


Topic difference: We expect multi-page documents to comprise a rather coherent topic structure.

for each page p, we determine the difference between its topic composition θ p and its predecessor θ p−1 as a third feature type for PSS.


This could be due to Y containing fewer borderline cases.

we do need positive examples drawn from Y or M, as well as negative examples drawn from N. It seems to make less difference whether we consider M to be positive or negative examples and we may be able to exclude either Y or M in training.


Furthermore, the collected documents are associated to a variable number of translations as well as variable sets of meta data that is used for indexing.

the collecting and managing of such data is a great burden and consumes a lot of time and computing resources.


In addition, the authors discover that this perturbation affects various models trained on the same dataset.


Moreover, it is possible for commenters to believe that they are witnessing a trolling attempt and respond accordingly even where there is none due to misunderstanding.

the inclusion of comments that do not involve trolling would allow us to learn what triggers a user's interpretation of trolling when it is not present and what kind of response strategies are used.


Up-to-date information about conferences is important for scientists who track conferences of their interest and check, e.g., dates of a conference, deadlines, that could change especially during submission period.

a system that gathers such information could ease scientists' lives.


However, we could not find any publicly available corpus for scientific conferences.

we created an annotated corpus for the task of information extraction from homepages of scientific conferences.


For instance, there are different ways of writing dates, the names of the conference provided in WikiCFP could differ slightly from the name on the page.

we employed the following method for name of a conference comparison.


For instance, a sequence International Conference on Artificial Intelligence & Applications may have the following entity types assigned: International -conference name, Conference -conference name, on -other, Artificial -conference name, and so on.

we expand a base object of a model to be a sequence of tokens that groups words forming one instance of entity.


The results show that each group of features carries some information that is important for (at least one) interesting entity type.

we could say that it is crucial to prepare features that are specific for a given domain.


The word Cattle has a significant presence in the document.

the system not only correctly predict British cattle as a keyphrase, but also erroneously predict cattle industry, cattle feed, and cattle brain as keyphrases, yielding overgeneration errors.


Parsed information is represented in RDF which can either be downloaded or queried online via SPARQL.

the focus is on explicating information which is hidden in uni-or semi-structured text.


Populist parties use these feelings of unease among the public with oneliners that fit well into the media logic of the media.

negative stereotypes and prejudice can take hold of the public which may very well harm relations between communities and provoke societal conflict (Rehman, 2007).


We were unable to find a finer-grained distinction of types of friends reliably across the whole corpus.

the friendship sub-categories are not available and the conversation is labeled as "friend" in both cases.


5 To elicit the semantic types relevant for the history domain, we combined knowledge from domain experts and a corpus driven approach based on a wide reading of entries, aimed at validating and increasing the initial proposed classes.

we conceived seven classes, presented in Table 1.


The main fallacy of methods in this approach is that they rely on the morphosynactic analyser of the term extractor that does not recognize all candidate terms and those chunk-based methods, having extended the alignment model with automatically extracted language pair specific rules.

this method blurs the distinction between terms and non-terms.


However, term extraction along this line is often limited to noun phrases (< 5 words) from monolingual comparable corpora.

the recall of such an approach is not satisfactory under some circumstances.


N-gram technique is commonly used as a languageindependent approach, particularly for under-resourced language.

the term candidate classification is framed as a N-gram classification task rather than the conventional sequence labelling methods that are commonly seen in previous work (Zhou and Su, 2004;Finkel et al., 2004).


Eastwards, we distinguish the Zürich group from the easternmost group around St. Gallen.

for training and testing MT on various dialects, we consider in what follows six main variants of Swiss German, represented on the map in Notations.


However, only a small number of the population can communicate in both the languages.

to smoothly carry on the official government communication with the public, dissemination of information has to happen in both the languages.


Yet, the context with respect to official government letters, was quite different.

these were categorized as out-domain data.


), and ranks links by the probability that they point to candidate translations and/or in domain pages.

it organizes the list of URLs to be fetched as a priority queue and guides the crawler to visit "interesting" pages first.


Since this is not the case for many multilingual web sites, during the evaluation of the pair detector we omitted the tool's methods that exploit special patterns in URLs and links that point to translations.

we only used methods that are based on a) cooccurrences of images with the same filename in HTML source, b) edit distance of sequences of digits in the main content of webpages and c) structural similarity.


The overlap was found to be 44.5% in unigrams, 19.2% in bigrams and 5.3% in trigrams.

existing speech corpora in Modern Standard Arabic will be unsuitable for recognizing dialectal Arabic.


Next, we look at the languages for which not all genres are covered in the training data.

we can only train classifiers for two (Bulgarian) or three (Persian) of the genres in the test set.


News Commentary: The Chicago School claims that realworld market economies produce roughly efficient (socalled "Pareto optimal") outcomes on which public policy cannot improve.

any state intervention in the economy must make someone worse off.


Splitting the resulting corpora after insertion was therefore liable to separate a large proportion of sentence pairs.

we randomly split the documents of the corpora before parallel sentence pair insertion.


Choosing appropriate extraction strategy depends greatly on the task at hand.

if the goal is to extract bitexts for dozens of language pairs automatic approaches may come in handy.


a Russian version of a page contains a direct link to a Kazakh version of the same page and vice versa.

we handle document alignment at the stage of crawling.


To see how the two different ways of crawling affect the final quality of the bitext we applied both methods to the four websites and obtained two sets of parallel documents, which we proceed to split into sentences using NLTK Punkt tokenizer (Kiss and Strunk, 2006), and align with Hunalign (Varga et al., 2007).

we end up with two sets of bitexts which differ only in the way they were crawled.


Although manual evaluation is the best way to estimate alignment quality, it requires significant time and effort.


These discrepancies are due to differences between the morphological theories adopted by the UD treebanks developers and those employed by the developers of the morphological analyzers.

the adapted resources we provide are non-trivial to obtain, and required careful alignment of the morphosyntactic analyses with their UD treebank counterparts.


In fact, the extensive coverage of archaic vocabulary in SGJP is usually a curse when processing contemporary data, but in the case of 19 th c. it is actually a blessing.

the morphological dictionary needed very few lexical additions to cover 19 th century vocabulary of the corpus.


each word form), the processing response of the map is represented by the synchronic activation pattern of all the BMUs that most highly get activated for that input sequence.

the task of word recall tests how accurately a map can retrieve the input word from its synchronic activation pattern, namely how accurately the activation nodes of the map can encode information about the timing of the input symbols that make up the word.


Remarkably, both German and Greek exhibit systematic (albeit not always predictable) processes of stem formation, followed by a fairly homogenous pool of inflectional endings.

in both languages, the base stem (or present stem) is often followed by a highly embedded and unpredictable sequence of symbols which account for the negative slopes in the corresponding segments.


This reduces processing uncertainty, by constraining the range of possible continuations at the stem-suffix boundary of irregularly inflected forms.

irregulars tend to blur the TSOM sensitivity to the verb morphological structure, favouring a somewhat more holistic processing strategy.


5 It represents a graded -and continuous -meausure of paradigmatic (ir-)regularity that considers, for each inflected form, the number of stem-sharing forms (or stem family size), instead of a dichotomous and formal classification of paradigms (regulars vs. irregulars).

given the number of inflected form-types for each paradigms, the average stem family size correlates better with the non-categorical idea of inflectional complexity.


We are comparing a list of lemmas with a list of word forms, this means that not every full form can be covered.

the overlap is a good start, especially as (longer) word-formation products could be analyzed in combination with a word splitter for flat structures.


These four datasets were selected for being consensual gold-standards for the evaluation of semantic similarity and relatedness models.

the problem of measuring the semantic similarity and relatedness of two concepts can be stated as follows: given two concepts A and B, determine a numerical measure semantic similarity is considered a particular case of semantic relatedness.


Our results show that we can achieve higher accuracy when DA POS tagger is used.

our Gulf POS tagger has achieved 91.2% accuracy for POS tagging GA using Bi-LSTM, which is 16% higher than the state-of-theart MSA POS tagger.


Since Wasim uses morphological analysers, if the annotator chose one solution, it will affect multiple tasks at the same time.

we allow the annotator to edit previous tasks without leaving the screen.


Obviously, the speed of annotation depends on several factors like text, language, course vs fine-grained tagging, and annotator experience.

reported speed measures should be viewed with caution.


When annotating a historical language, the annotators lack the intuition of a native speaker.

decisions about a category can only be based on the comparison and statistical analysis of texts (Dipper et al., 2013, 2).


Furthermore, the annotators might not be able to distinguish these cases from other cases, where the ambiguity cannot be resolved.

hiNTS -which is derived from hiTS for the annotation of GML texts -is designed following two principles: on the one hand, it aims to allow the annotator to assign tags that are not fully specified while on the other hand it allows the annotator to be as specific as possible.


Since the annotation of the texts should be used for a new grammar of GML, one of our most important requirements is that some decisions should not be made before annotating but be left for later studies in order not to anticipate the results coming from the analysis of the annotated texts.

in the POS part of the tagset, we avoid lexeme-based assumptions (as for the group of determiners and pronouns) and prefer structure-based rules.


The necessity for a tagger that is compatible with this tag-set -as well as with the various transcription conventions that have been put in place for marking-up spoken contributions to the corpus -was an influencing factor in our decision to create our own POS tagger rather than to rely on an existing solution.

it is from this bespoke tagset that the POS categories CyTag assigns are selected.


There are a number of particularly encouraging observations that can be made about the results presented in Section 4.. As Table 5 demonstrates, CyTag also performs well -with F1 scores approaching 95% -on only those tokens which had more than one reading prior to CG being run.

while it is true that a large number of tokens are disambiguated by default as a result of only having one possible reading to begin with, the overall results are not necessarily skewed by this, and our CG-formatted rules are able to prune the more ambiguous tokens with good effect.


Tamil is a low resourced, morphologically rich language with many inflections and a complex grammatical structure.

automatic POS tagging for Tamil is a challenging task.


Our research establishes the fact that graph based semisupervised approaches are more robust than supervised classification algorithms for POS tagging when the data set is relatively small.

graph based semi supervised data can be employed in the early stages of creating POS tagged data sets.


Human annotators can correct the automatically annotated corpus with less effort, and the corrected annotated data set can be used in an iterative manner to re-train the tagger.

graph based semi-supervised approaches are particularly useful for POS tagging of low-resourced languages such as Tamil.


The main limitation of this technique is the amount of time taken to build the graph.

we intend to look into different code optimization methods.


By adapting the system's behaviour to the user, the conversation agent may appear more familiar and trustworthy and the dialogue may be more effective.

current research focuses on user-adaptive Spoken Dialogue Systems, e.g.


Moreover, the aim of this work is to identify what causes the differences in communication styles except for the user's cultural background.

we explore not only the influence of the user's culture but also of the gender, the frequency of use of speech based assistants as well as the system's role.


Our aim was to explore the influence of the user's culture and gender, the frequency of use of speech based assistants as well as the system's role.

we created three dialogues with different options for the system output and conducted a user study with 339 participants from Germany, Russia, Poland, Spain and the United Kingdom.


An end-to-end training method also needs a corpus for training for the Natural Language Generator (NLG).

we also explore which crowdsourcing design yields the best conversational utterance data for training the NLG.


For each hotel in a random selection of 200 hotels from the paraphrase experiment, we selected content for both rooms (4 attributes) and properties (6 attributes) by picking the attributes with the highest scores (as illustrated for a small set of attributes in Table 2).

hotel has two unique content tables assigned to it, one pertaining to the hotel's rooms, and the other for the hotel grounds.


Our assumption is that more important attributes should be presented earlier in the dialogue, and that a user-system dialogue simulation system design (Shah et al., 2018;Liu et al., 2017;Gašić et al., 2017) would require such information to be available.

in order to provide more information on the importance of particular attributes, we analyze where in the conversation (i.e.


One promising area of inquiry is the sharing of personal information between the conversational participants.It has been observed that people frequently focus on sharing personal information about themselves in chat-oriented dialogues (Mitsuda et al., 2017).

this provides evidence that focusing on topics related to personal information will cover a significant portion of topics relevant to chat-oriented dialogue.


In the FactBank (Saurí and Pustejovsky 2009), the attributed span itself is not marked, but its events are linked to their source by introducing predicates in order to derive their factuality.

the annotation schema resulted in a relatively high inter-annotation agreement %81.


This result is justified by being the default label of the source is the writer in the tool for each instance and the annotator should change it when appropriate, but he missed doing so for this feature.

we will make this feature without default value in the tool.


Not surprisingly that supplement features of (cue, source and content) have more disagreed instances because we did not limit them to temporal or relative clauses only.

annotator sometimes is confused whether include the clause into cue, source or content themselves or leave them to the supplements.


One purpose for the annotated corpus was to train a spoken dialog system which would be able to play the roleplaying game described above with a human partner.

lexical tags were added to the information transfer dialog acts whenever relevant information was included in a participant's contribution.


Morphology is important particularly for intra-sentential connections.

we distinguish a set of intra-sentential DCs, referred to as subordinators subsuming postpositions (için, 'in order to, for the purpose of', gibi 'as, like') and converbs (e.g.


In Turkish, when a converb has a DC role, it is always an explicit intra-S connective.

with the addition of converbs, we extend the coverage of explicit intra-S DCs in TDB 1.1.


9 We note that most of the senses in Table 7 can well be conveyed by means of an AltLex containing the core lexeme of the intra-S connective, as in the use of the postposition 8 TDB allows assigning multiple senses to a discourse relation.

in Table 6, overall, there are more senses than the number of discourse relations.


Our findings suggest that whether or not a given explicitly marked relation holds inter-or intra-sententially may carry valuable information for Turkish as well.

the reported results are promising for future efforts on automatic sense disambiguation of explicit discourse connectives in Turkish.


However, P1 is required in the TextGrid format, as it is the segment used for further computation of relative prosodic parameters, as will be introduced in the next section.

the proposed annotation includes two tiers for each level of representation: one for propositions (e.g., L1P) and one for thematicity labels (e.g., L1T).


In other words, the computed minmax score provides information on the location of the F0 peak ('maxF0.t') and intensity valley ('minint.t').

if an F0 peak is located within the first half of the time span, it will have a score between 0 and 0.5, and if an intensity valley is located within the second half of the span, the score will be between 0.5 and 1.


After the recording, all the recorded sound was mixed, with the noise removed.

15 expert SCs and 79 visitors participated in the recordings, and 35 sessions were recorded in total.


Second, by not explicitly referencing the move to the next exhibit and by continuing to talk to the visitors while moving, the SCs implied that walking per se was not central to their activity; rather, walking was an interactional forum in which their main activity occurred.

even if the F-formation was dissolved, the SCs could maintain their scientific communication.


Naturally, we can use the text of each dialogue turn, and possibly of nearby turns.

we rewrite the feature function as f i (y t , y t−1 , x t ).


Answers with different scores should definitely go into different clusters, answers with the same score may or may not belong to the same cluster, dependent on their semantic relatedness.

we cannot use categorical seed information to estimate the number of clusters and to initialize seed clusters as it is done for instance by Basu et al.


This is because we thought that the word range of MacArthur-Bates CDI was insufficient to grasp detailed changes and individual differences in vocabulary development.

using such a new vocabularychecklist, we create a large-scaled database of vocabulary database in Japanese-speaking children.


More generally, EDM also allows us to study the effects of the educational support provided by the system, and to advance scientific knowledge about the process of language learning and acquisition.

the final aspect of Revita to which we draw attention in this paper is that as the platform is used by the learners, over time it builds a rich and valuable resource: the detailed educational data in the form of learner responses-correct and incorrect,-the contexts of the responses within the stories, all words which were unfamiliar in their contexts (i.e., for which translations were requested)-as well as the timings of all of these interactions.


For verbs, these are: -VAFIN: finite auxiliary -VAPP: past participle of an auxiliary -VMFIN: finite modal auxiliary -VMINF: infinite modal auxiliary -VVFIN: finite lexical verb -VVIMP: imperative of a lexical verb -VVINF: infinitive of a lexical verb -VVPP: past participle of a lexical verb As described in the Introduction, finite German verbs can occur in different sentence positions.

we additionally coded the syntactic position of finite verbs as follows: sentence-initial "V1", "V2" and "sentence-final" (or "other" in case the syntactic structure was unclear due to elisions, noise, etc.).


Tomasello (2009), Cohn (2011), and Theakston and Lieven (2017)) and are not hardwired into our genetics.

one area of important research is to examine CDS from typologically maximally diverse languages and to identify distributional patterns in the input that appear cross-linguistically.


In the absence of well-established written traditions and a single authority, the regional languages of France do not have unanimously recognised and accepted standards (Caubet et al., 2002;Vannin et al., 2012).

the transcription solutions proposed vary from one language to another, even among the territorial languages of France, the spellings of which are based on the Latin alphabet and are meant to be close to the pronunciation (at least to a minimum).


The Alsatian dialects do not have a standardized spelling system, despite a literary history that goes back to the beginning of the 19th century.

writers often use their own spelling systems, more or less based on German and often with some specifically French characters.


Seneca has fifty-two possible pronominal prefixes (varying by person, number, gender, and other features), thirty prepronominal prefixes (including verb tense, case, and other grammatical features), and four aspect suffixes (including ongoing action, completed action, habitual action).

for a given verb there can be as many as 4680 different forms -not including any potentially incorporated nouns -which stands in stark contrast to a morphologically poor language such as English, in which a regular verb can have up to only five possible forms.


Should considerations be missed or ignored and the planning decision challenged, the council would be liable for any compensation due to the decision being changed.

the laborious and meticulous process of analysing each comment is required; however, the benefit of support through summarisation and visualisation tech-niques would increase the efficiency of dataflow through the organisation and identify potential risk areas.


4 SALDO contains all parts of speech, not only the open lexical classes.

the noun yta 'surface' has as its primary descriptor the preposition på 'on'.


Since the BWS values were created only for the entries annotated as non-neutral by the DA scoring (|value| ≥ 0.5), they cannot all be used for testing (or else the training set would be too biased towards neutral elements).

the intersection of the DA test set and the entries with BWS value is used for applying the rank-based measures.


The main goal is to extract a subjective value from the text, that might include a certain polarity degree, and it could be focused on a particular entity or aspect (Liu, 2012).

it is likely to be a special difficulty in summarizing user opinions from social media, as there is not an specific order among all the opinion posts grouped by the same topic.


Even though we have covered the elements that annotators where writing about, that is not complete enough as people express themselves with a sentiment in mind that is reflected on the way each annotator communicates and the words they use.

we had to analyze the sentiment orientation of each summary in order to get the com-plete meaning of each summary.


None of the choices made from the annotators could be depicted a priori as right or wrong.

a good improvement in the application of the scheme would be that of accepting the labeling of two or more categories from each annotator on a single tweet.


While performing a linguistic analysis of the TWITTIRÒ corpus, we observed that syntax plays a significant role in the activation of irony in Italian, especially in social media short messages.

we are planning to enrich our actual dataset with additional syntactic information such as Part-of-Speech tags and syntactic relations in Universal Dependencies (UD) format.


The authors concluded, that the way a given wordnet has been built seems to affect the propagation performance.

we should not apply the same propagation scheme for every wordnet.


If two polarity values have the same total weight, we apply the following rules to solve this discrepancy:    From the structure of plWordNet we selected only the most frequent relations as a basis for features.

the selected relations cover more than 95% of all relation instances (occurrences) in plWordNet: hyponymy, hypernymy, fuzzynymy, similar_to, feature_value, meronymy, holonymy, collection_meronym, collection_holonym, type, member, taxonomic_meronym, taxonomic_holonym.


First, we used a simple generalization function, to assign the polarity to the synsets, depending on the polarity of their LUs (see Section 4.1.).

the original a 5-degree scale of sentiment polarity intensity was projected onto the coarse-grained 3-degree scale.


The focus of the data collection described in this paper was on obtaining training data for the SLR system.

in an attempt to reduce the number of instances in which no sign was produced at all, the participating signers were provided with the test items prior to the recordings in the form of a list of glosses with accompanying German example sentences.


Our analysis showed that the distinction between permissible variants (Category 2) and incorrect productions (Category 3) of a sign was in some cases especially challenging.

cases for which the team was uncertain were extracted for presentation to a group of seven outside sign language experts.


We show here how this tool facilitates theoretically-informed typological and historical analysis of sign languages, using the interface to systematically investigate the degree of similarities across 23 SLs.

our methodology implements Woordward's original idea of comparing pairs of sign languages in such a way as to conduct an effective cross-linguistic comparison of a large sample of SL.


Contrarily to spoken languages, SLs have no written form which can be read out and captured without a trace of the preparation.

prepared discourse usually has either to be delivered by heart, or to work around the lack of written support.


In summary: • only note-supported footage can reliably yield long yet fully fluent discourse; • only non-supported signing can be analysed as fully acceptable language productions; • memory allows for non-supported but short stretches of clean discourse; • any break in the flow of discourse should be identifyable in the resulting data.

our proposition to collect long and fluent elicited signed discourse is: • submit the assignment in advance to the informants, together with a description of the following rules, for them to prepare their intended productions; • allow any preparation notes, drawings, personal recordings, etc.


This creates nonlinear, multichannel dependencies that (a) complicate the translation of SL as compared to spoken languages and (b) require specific descriptions for motion synthesis.

temporal and spatial relations of generated signing motion sequences might easily appear artificial and unnatural when applied to three-dimensional avatar animations (Kacorri et al., 2015).


Here, it is particularly important to ensure the accuracy, density and completeness of the recorded signing data up to subtle finger and facial movements and the repetitive occurrence of NMSs with wide intra-feature variability.

the presented deep JSL Corpus (JSLC) constitutes a collection of natural JSL sentences recorded with a multi camera optical motion capture system and includes various types of NMSs as well as variations in sentence intonations and signing speed.


Such sparsity in the vocabulary domain and the absence of NMSs is likely unsuitable for the deep learning of signed sequence connections.

we built the present deep JSLC in a different way that is not known to be reported similarly by any other research yet.


To evaluate the potential benefit of deep sequence generation techniques for natural sign display, it was consid-ered sufficient to first train networks on a smaller subset of JSL vocabulary that could be extended for better generalization later.

we defined the proposed deep JSLC as a collection of sentence expressions and multiword phrases within a dense vocabulary domain.


However, none of the proposed studies has yet succeeded to fully describe the specificities of SL, such as the use of the signing space and the simultaneous articulation of multiple channels.

researchers in Sign Language Processing (SLP) must model languages without fine linguistic descriptions, without a written form and with a limited amount of corpus (Cuxac and Dalle, 2007).


Middag in 2012 presented a new method that predicts running speech intelligibility in a robust way against changes in the text and against differences in the accent of the Dutch speakers applicable to patients treated for HNC.

our hypothesis is that an automatic assessment technique can measure the impact of the speech disorders on the communication abilities giving a severity index of speech in patients treated for HNC and particularly for oral and pharyngeal cancers.


The first is that it is very laborious and time consuming; on average, manual segmentation can take up to 30 s per phone (Leung and Zue, 1984;Stolcke et al., 2014) to segment.

newly recorded speech data cannot be used quickly if manual segmentation is desired.


However, in this study, we only consider content words.

the denominator is relatively small; thus, the proportion of missing translations becomes relatively large.


Another reason is that names that are unfamiliar to the interpreters and complex numbers, which likely increase the workload of interpreters, seldom appear in the speech in the analyzed data.

names and numbers do not trigger problems for interpreters.


Indeed, even if the speaker is very talented and coherent along the books, she can for example forget to change her voice when comes a new character.

for such speech intervals, the timbre remains the own speaker's timbre or corresponds to another character.


As mentioned previously, label combinations were used during the annotation phase to better characterize some expressive content.

these annotations are considered as new emotional labels which can not be merged with single labels easily .


Emotional segments are defined as segments consisting of an homogeneous emotion, be it characterized by single or combined labels.

there is not constraint on segments' duration.


Therefore, there is not constraint on segments' duration.


In order for the protocol to be usable in a large set of contexts (clinical practice and clinical studies), regional variation needs to be covered.

references were seeked for speakers from four French-speaking countries, as described below.


The resulting distribution among classes is highly unbalanced (L 1 : 109, L 2 : 3.051, and L 3 : 916).

it is strongly recommended to apply resampling techniques before classification models are trained.


A camera's zoom changes the focal length of the lens, and thus the angle of view and picture frame; whereas the zoom feature in a graphical user interface typically changes the view of a document.

the interpretation of a request like zoom in on the man depends on context: as a standalone request, it probably represents the intention of changing the picture frame; but in interactive dialogue or as part of a multi-instruction sequence, it could indicate a request to change the view of the image in the editing interface.


On the other hand, comparing each two documents is a costly and time-consuming task, especially as the number of documents grows.

it is important to find a balance between the scale of redundancy detection comparisons and the degree of redundancy that is acceptable in the resulting corpus.


However, this are still more than 20 million sentences (see Table  4).

the corpora are still large enough for statistical analyses.


a significant co-occurrence on the left and the right side).

we normalize the frequency of the significant co-occurrences for each word by 2n.


Although the corpora mentioned in this section have been useful in LT and language research they lack in size and/or coverage to fulfill the requirements that present day LT makes.

it was considered necessary to embark on the project of compiling the IGC.


The equivalence of all object properties within the BantuLM vocabulary is created with the owl:equivalentProperty property.

all definitions of the classes and properties need to be obtained from the interconnected original vocabularies.


Further, it can be seen that this WordNet entry ultimately 16 leads to a sense definition of the lexeme reporter.

the interlinking of the Xhosa English translations with the WordNet RDF lexical entries, consequently, leads to an enrichment of the Xhosa noun and verb lexemes with corresponding lexical senses.


This is especially problematic in a larger context where rights on relevant resources are held by different institutions with a varying degree of openness and each providing their own proprietary access interfaces.

of this rather typical situation many large-scale infrastructures in the field of linguistic resources promote the usage of service-oriented architectures (SOAs) that provide data and services via standardised Web interfaces and data models.


'These initiatives create a more mobile work environment and consequently reduce our housing footprint.'

the lexicon reflects the decisions taken in the treebank: we describe the intrinsic values of the DM independently of values that may be triggered by adjacency between sentences and by the lexical content of the clauses.


One major challenge to develop NLP techniques for lowresource languages is that system developers usually have little knowledge about the languages so it's very difficult to perform effective error analysis in order to do hill-climbing.

in this paper, we ask two Uyghur native speakers to focus on detailed error analysis.


For example, "donald trumpni" refers to "Donald Trump", "nato ken" refers to "Naoto Kan", and "amerika" refers to "America".

we tried to use a Soundex based matching method to perform cross-lingual entity linking on each romanized Uyghur ngram to English Knowledge Base and English gazetteers in order to determine whether it's a name.


Most documents of requirements often contain restricted information, and documents that we used for evaluation data are also confidential in which we cannot directly release them.

in this paper, we perform the data anonymization process for ReQA evaluation data, and we release them for a reproducible automatic error detection task.


The loss of this information can lead to significant financial losses and reputational damage.

the accurate data anonymization allows to preserve the confidentiality of requirements documents and makes them useful as a linguistic corpus for a Natural Language Processing (NLP) task like automatic error detection.


The current datasets consists of 152 documents with more than 131, 000 words.

far, 2, 908 entity mentions (2, 191 organizations, 1, 717 products) have been annotated, and a total of 379 CompanyProvidesProduct relation mentions.


Moreover, the HAREM classifies 10 categories of NEs (Person, Place, Organization, Value, Time, Event, Abstraction, Work, Thing and Other).

the HAREM presents a more demanding task and, therefore, the performance values obtained using its reference data sets are still lower compared to the others (Santos and Cardoso, 2007).


We also observed that some NEs of the Person category are preceded by words such as diz (say), explicou (explained), afirmou (said), etc.

the graphs created capture some simple heuristics to the recognition of NEs in the training set.


Additionally, the state-of-the-art systems for downstream Chinese tasks, such as NER, may not use words.

we use character embeddings for our systems instead.


However after examining the results it's clear that while more correct entities are being classified, there is also increased levels of over-classification which can be detrimental to the system.

we aim to increase our accuracy with an with an empirically derived threshold of 150.


This implies that the representations learned from the source dataset are efficiently transferred and exploited for the target dataset.

when transfer learning is adopted, fewer annota-tions are needed to achieve the same level of performance as when the source dataset is unused.


To preserve the original information, nodes on the surface layer are explicitly referred to from this node.

there are two links from the node "street" to the surface layer: to the noun "street" and to the preposition "on".


Since the fundamental challenge is the insufficiency of splits, the splits which can undergo further splitting themselves are likely to be of greater length than fundamental morphemes.

using the length of the split words as a heuristic, a total of 3, 959 examples are reduced to 2, 700 where further splitting is applicable.


If any of the option gives the correct word merging, the Sandhi is marked as correct.

the results are presented by combining the results of both internal and external Sandhi options in the most optimistic manner.


All three annotation campaigns have been done by the experienced PDT annotator.

we are not able to provide inter annotator agreement.


This also ensures that paragraphs, sentences and tokens are correctly and uniformly split, and represented in the standard CoNLL-U data format.

1 all the annotation layers can be afterwards merged based on the document, paragraph, sentence and token identifiers.


Conveniently, phrasal constructions in the hybrid trees correspond to isolated subtrees in the UD trees.

every phrase can be transformed by considering only its constituents and its parent (or the direct ancestor if the parent is a coordinated constituent).


The semantic roles in PropBank are much more robust compared to FrameNet, and the overall PropBank annotation systematically follows the verb argument structure.

the PropBank layer can be semi-automatically derived from the FrameNet and UD layers, by providing a mapping configuration from lexical units in FrameNet to PropBank frames (see Table 4), and a mapping configuration from FrameNet frame elements to PropBank semantic roles for the given pair of FrameNet and PropBank frames (see Table 5).


It is related to the subject huzi 'beard' instead.

we labeled an 'of-asso' dependency between the extracted topic and the subject of the relative clause.


In (8), the direct object shiqing 'things' is moved to the front of the sentence.

a label '2' is given to the dependency between pengdao 'came-across' and shiqing 'things'.


Dependency parsers, such as MaltParser (Nivre et al., 2006), Mate (Bohnet, 2010) and MSTParser (MacDonal and Pereira, 2006), can be trained directly on Stanford dependencies to predict Stanford dependencies.

we implemented a heuristic extraction of nonlocal dependencies from gold Stanford dependencies and evaluated the results against our annotations.


We did not have the resources for manually annotating the whole F30kE caption corpus with syntactic annotations.

we limited our effort to the manual annotation of PP-attachments in ambiguous contexts.


Qualitative methods are time-consuming and require manual work, thus, it is impossible to apply them to large amount of data.

it can be argued that the made observations lack statistical significance.


Moreover, we found that many glosses are in Latin rather than Modern German, and often not in line with modern orthography (for example, wonung for Modern Wohnung).

we consulted Köbler (2014) as a source of secondary evidence.


While the parameter vectors of DIFF and CONCAT can be interpreted as distributional prototypicality, these methods do not consider interactions between x and y.

they cannot capture the relation between the word pair.


In training data, the number of types of hypernyms is small, and some types appear many times, while the number of types of hyponyms is large, and each type appears only a few times.

words with a hypernym position have tendencies such as domain similarity in addition to the expected features of the prototypical hypernymy, while words with a hyponym position have few tendencies.


This is the opposite trend as what was seen in Section 3.1.2.

it seems that the overfitting of hypernyms is alleviated.


Currently, we focus on the branches of the BLL Thesaurus containing language-related information and are working on the integration of the relevant subject terms into the BLL Ontology and their mapping to the LLOD vocabularies Lexvo and Glottolog.

we will achieve broader coverage, enable finer-grained queries, and include more LLOD resources into the portal.


We, by contrast, model the BLL language identifiers as ontological classes (e.g., a class German with subclasses HighGerman and LowGerman) following the previously described methodology.

we provide a consistent ontological representation for all subject terms, avoid splitting the thesaurus into heterogeneous fractions, and are able to use the same standard reasoning principles across all branches.


However, the establishment of links between BLL classes and Lexvo or Glottolog instances may lead to formal incon-sistencies.

we made several adjustments to our model.


In our initial quest for finding a reusable wordnet browser, we end up coming across a number of browsers (addressed in the next section) that had some features that others were lacking and vice-versa.

of this exercise, we eventually drew a list of all those should-have requirements, which was further extended with possible other features that a general purpose browser should have.


If a truth-test is proved to be entailed by the ontology, then we decide that the knowledge in the WordNet pairs that yield the corresponding CQ is compatible with SUMO and the mapping information.

we conclude that those WordNet pairs are well-aligned -or simply alignedwith SUMO and the mapping information.


On the contrary, if a falsity-test is proved to be entailed by the ontology, then the knowledge in the WordNet pairs that yield the corresponding CQ is incompatible with SUMO and the mapping information.

we say that those WordNet pairs are misaligned with SUMO and the mapping information.


Similarly, the synsets in the pair member(police officer 1 n ,police force 1 n ) are respectively connected to PoliceOfficer a = and PoliceOrganization c +.

by applying the second QP we obtain the following CQ:  The above CQ is resolved by ATPS since its truth-test is proved to be entailed by Adimen-SUMO v2.6.


Thus, by applying the second QP we obtain the following CQ:  The above CQ is resolved by ATPS since its truth-test is proved to be entailed by Adimen-SUMO v2.6.

the pair member(police officer 1 n ,police force 1 n ) is decided to be well-aligned with SUMO and the mapping information.


However, FruitOrVegetable c is defined to be subclass of CorpuscularObject c in SUMO.

fruitOrVegetable c is incompatible with Substance c , which prevents the use of material r for the instantiation of the third QP.


When we say that a sentence is 'understood', we mean that the concepts and the conceptual relationships expressed by the sentence are unambiguously identified, and we can make correct inferences and/or responses.

to achieve natural language understanding, computer systems should know the sense similarity and dissimilarity of words and sentences.


For instance, we established the links between HowNet sememes and WordNet synsets.

wordNet synsets (version 1.6) are used as an alternative intermediate representational language.


Furthermore, it is debatable whether there exists a limited and fixed set of so-called primitives.

we adopt 2,233 primitives from HowNet and extend 2698 basic concepts which make a deeper hierarchical structure and more precise semantic branching.


In E-HowNet 2.0, {狗 |dog} is a basic concept under {livestock| 牲 畜 } and defined as {livestock| 牲 畜 :telic={TakeCare| 照 料 :patient={family| 家 庭},agent={~}}}.

the top-level definition of Beijing dog 北京狗 is as (1a) and can be further extended into the ground level definition as (1b).


Actually, there is no clear-cut distinction between the two classes.

by adding the hierarchy for relations, E-HowNet provides a uniform representation for both function words and content words and enable the capabilities of semantic composition and decomposition.


A lexical word may play different syntactic and semantic functions and ambiguously denote many lexical concepts.

in E-HowNet, each lexical concept of a word is identified and provided with its sense definition, English translation, part-of-speech.


However, 'teacher', also denotes a kind of occupation and should be regarded as an 'occupation value' as well.

we mark the semantic function of ' teacher' as {OccupationValue|職業值}to include both meaning facets.


From the word similarity point of view, the degree of similarity for 墨鏡"sunglasses" and 專利"patent" should not be high no matter which ontology is applied, for the former is a concrete object but the latter is an abstract one.

their distance in an ontology is also far from each other.


Such dynamic semantic clustering search can achieve a comprehensive hierarchical overview for words with the same sematic relation and provide a practically useful new query tool for lexical semantic studies.

we also developed the E-HowNet Relation Database 3 to achieve 3 http://ckip.iis.sinica.edu.tw/~mhbai/relation/ this pupose.


Some thesauri such as the IPTC 3 contain terms for calamities but these are not formalized and provide few relations.

we decided to define a new model, reusing existing resources as much as possible.


Computational models and dialogue systems that utilize this framework must operate at the millisecond time scale, so the number of features to consider must be kept to a minimum.

cues from the visual modality (e.g., gaze) are absent from our scheme due to computational complexity, even though they have been identified as important for managing turn-taking in face-to-face interactions.


However, human experts in multiple languages are hard to come by.

the community would benefit from sharing of such manually created resources.


Many concepts in the Indian languages are specific to the Indian culture.

their corresponding variant is not available in the Princeton WordNet (and is not likely to be included anytime).


In this application, users are able to evaluate only what they see, and generally have no idea what else is present in the collection.

precision is of far more interest to customers than recall.


The PictureQuest system uses a semantic net based on WordNet (Fellbaum 1998) to expand terms.

a query for car or automobile will retrieve essentially identical results; vehicle will be less accurate but will still retrieve many of the same images.


We can assume that the non-Englishspeaking user will, however, recognize phrases in her or his own language, and look them up as phrases where possible.

we can expect at least those multiword phrases that have a dictionary entry to be correctly understood.


Even if the translated word is not a true translation but is strongly related to the original query, it is still helpful.

cLIR is a suitable application for such a translation model.


This fact is used as the criterion in searching for candidate sites.

to determine possible sites for English-Chinese parallel texts, we can request an English document containing the following anchor: anchor : "english version H ["in english", ...].


For our purpose, however, we need to explore the sites more thoroughly using a host crawler.

we continue our search for files with a host crawler which uses the documents found by the search engines as the starting point.


Some customer service providers have started to take advantage of the recent advances in speech recognition technology.

some of the IVR systems now allow users to say the option number (1, 2, 3 .....


For example, if one wanted to transfer $50 from one account to another, the system would first ask the account that the money was coming from, then the account that the money was going to, and finally, the amount to be transferred.

a user could not say "I want to transfer $50 from my savings account to my checking account" and have the system conduct that transaction.


The ID numbers were most often used for ordering the corresponding parts using an automated IVR system.

the system we have built helps automate some percentage of these calls by allowing the engineer to describe a part using natural language.


Each remaining word was inserted as the index of the appropriate hash table with the identification number of the part being the value corresponding to the index.

for each non-stop-word word used in describing a part, the hash table contains a list of all the parts whose descriptions contained that word.


The system was designed in a manner such that it could be easily ported from one application to another with minimal effort other than providing the domain-specific knowledge regarding the new application.

we decided to abstract away the domain-specific information into self-contained modules while keeping the other modules completely independent.


), the recognition quality is quite poor.

the context-based parser ranks the input received from the user using a sub-string matching algorithm that uses character-based unigram and bigram counts (details are provided in the next section).


It has to be robust enough to deal with poor recognition quality, inadequate information input by the user, and ambiguous data.

the DM is designed to handle these issues.


This is especially true when the DM uses heuristics, and sub-string matches (as in the case of product names).

the DM occasionally asks the user to confirm input it has received.


Since the type of input is known, the context-based parser uses a sub-string matching algorithm that uses character-based unigram and bigram counts to match the user input with the expectation of the dialogue manager.

the sub-string matching module takes as input a user utterance string along with a list of expected responses, and it ranks the list of expected responses based upon the user response.


The inputs were closed utterances, meaning the system already knew the utterances, enabling the utterances to be transferred at a good quality.

we used closed utterances as the inputs to avoid translation errors.


If we resolve the latter problem, that is, if we expand the transfer rules and the transfer dictionary entries according to the "participant's social role" (a clerk and a customer), then the recall rate and the precision rate can be improved (to 86% and 96%, respectively, as we have found).

we can say that our method is effective for smooth conversation with a dialogue translation system.


The advantage of using real dialogues between people is that they will illustrate which tasks and needs that people actually bring to a particular service provider.

on the level of the users' general goals, such dialogues have a high validity.


A possible disadvantage with using both Wizard of Oz-and real computer dialogues, is that users will quickly adapt to what the system can provide them with, and will therefore not try to use it for tasks they know it cannot perform.

we will not get a full picture of the different services they would like the system to provide.


Intentions, or plan steps that the agent has committed to but not yet acted on, are stored in an agenda.

the agent's partial plan for achieving a goal is a network of intentions.


In addition, there is no way for the system to ask the student a question or lead the student through a multi-step directed line of reasoning.

there is no way to use some of the effective rhetorical methods used by skilled human tutors, such as analogy and reductio ad absurdum.


These categories represent the possible transitions between states.

history can be stored, and context considered, only by expanding the number of states.


However, we extrapolate from this theory when we use the term conceptual dependency (tree) structure, which has no equivalent in MTT (and is unrelated to Shank's CD structures proposed in the 1970s).

the framework represents a generalized processing environment that can be reused in different types of natural language processing (NLP) applications.


But, as we already mentioned, part of speech taggers often require input text split into sentences.

there is a lack of available systems for splitting ASR text into grammatical clauses.


Someone who has never attended college can either not respond at all, or give a random (and false) reply.

incorrect presuppositions cause two problems.


Some of the human ratings diverged substantially.

we computed two restrictive measures based on the ratings to evaluate the performance of DP.


Since survey methodologists will decide whether the presupposition is really a problem, a higher false alarm rate is preferable to missing out presupposition cases.

the updated DP is an improvement over the first version.


Previous work has argued that initiative affects the degree of control an agent has in the dialogue interaction (Whittaker and Stenton, 1988;Walker and Whittaker, 1990;Chu-Carroll and Brown, 1998).

a cooperative system may adopt different strategies to achieve the same goal depending on the initiative distribution.


However, despite the limited prompt, the user provides a well-formed query in (11), triggering TakeOverTask.

mImIC answers the query and switches to an open-ended prompt in (13), relinquishing task initiative to the user.


[Answer] [LimitedPrompt] [TakeOverTask] [Answer] [OpenPrompt] This is because in the Dempster-Shafer theory, if the initial bpas or the bpas for a cue provide definite evidence for drawing a certain conclusion, then no subsequent cue has any effect on changing that conclusion.

mImIC will retain both initiatives throughout the dialogue.


Although one might not expect abstract syntactic structure to be determined until the Realisation module (or perhaps slightly earlier), the CGS system starts this computation in the Centering module.

in the reimplementation, the Centering module computes (very partial) abstract syntactic representations for the entities that will eventually be realised as NPs.


One important point these otherwise very diverse types of constructions have in common is that, being open sets, they cannot be listed in repositories.

their detection will require the use of grammatical tools of some sort.


Each of these two columns will be considered as a set in the mathematical sense.

the English sentence in figure 1 and the one given below are indistinguishable from TransCheck's point of view.


After each character typed by the translator, the system displays a proposed completion, which may either be accepted using a special key or rejected by continuing to type.

the translator remains in control of the translation process and the machine must continually adapt its suggestions in response to his or her input.


We observe that for the pair of languages we considered, model 2 improves the amount of saved keystrokes of almost 3% compared to model 1.

we made use of alignment probabilities for the other models.


We added a new module (Resporator) which annotates text segments with QA-Tokens using pattern matching.

the text "for 5 centuries" matches the DURATIONS pattern "for :CARDINAL _timeperiod", where :CAR-DINAL is the label for cardinal numbers, and _timeperiod marks a time expression.


The last step can be delicate, as its primary goal is to keep the customer satisfied.

human intervention seems mandatory to allow for individual, customized answers.


Thus the preprocessing results will often differ for e-mails expressing the same problem and hence not be useful for SML.

of the tests in our application domain, we identified a favorite statistical tool and found that task-specific linguistic preprocessing is encouraging, while general STP is not.


If subsequent components are very good at discarding implausible answers, then summarization methods may limit system performance.

we investigate next the use of two linguistic filters in conjunction with the query-dependent text summarization methods evaluated here.


In the TREC8 development corpus, for example, 36 of 38 questions have noun phrase answers.

we next investigate the use of a very simple linguistic filter that considers only noun phrases as answer hypotheses.


This indicates that the NP filter depends critically on the text summarization component.

we will continue to use querydependent text summarization in the experiments below.


A user might want to retrieve documents about 'seol'agsan(Seol'ag Mountain)', and then it is likely that the documents with seol'agsan-gugrib-gongwon' are also the ones in his interest.

it should be exactly segmented before indexing in order for the documents to be retrieved with the query Morphological analysis is crucial for processing the agglutinative language like Korean since words in such languages have lots of morphological variants.


However, it is difficult to look at a large size of corpus and to assign analyses to it, which makes it difficult to estimate the frequency distribution of words.

we need another approach for obtaining frequencies of nouns.


Such morphological ambiguity is caused by overgeneration of the morphological analyzer since the analyzer uses less detailed rules for robustness of the system.

if we examine and correct the results scrupulously, many ambiguities can be removed through the process.


It follows that two syllable nouns take an overwhelming majority in nouns.

there are not many such nouns in the simple nouns extracted by the manually corrected nouns (a set of distinct nouns).


The min-max composition is a suitable operation for this case.

we first take the minimum value from the function Word for each possibility of segmentation, and then we choose the maximum from the selected minimums.


sis2.., s,~, the composition is started from each si (i = 1... n).

the possibility that the individual syllable forms a word is recorded in the cell of the first row.


What is interesting here is that the procedure follows the dynamic programming.

each cell C~,j has the most probable segmentation result for a series of syllables sj ..... j+i-1-Namely, C1,2 and C2,3 have the most likely segmentation of sis2 and s2s3 respectively.


Especially, the algorithm made results good enough and the builtin dictionary supplemented the algorithm.

the methodology is promising and the segmentation system would be helpful for the application system such as machine translation and information retrieval.


The problem of over-generalization of speech grammars and related issues is well discussed by Seneff (1992).

speech grammars must often balance the conflicting requirements of • accepting a wide variety of sentences to increase flexibility, and • accepting a small number of sentences to increase system accuracy and robustness.


We also discussed an MDL based algorithm for grammar compaction to reduce the size of the revised grammar.

using a combination of the algorithms presented in this paper, one can obtain a compact grammar that is guaranteed to disallow the counter-examples.


The rules within a block are ordered in decreasing order of confidence and specificity.

rules with conditions that check explicit inflectional paradigms of known roots are ordered before rules that guess the inflectional paradigm from the spelling of the root, and rules with more specific conditions are ordered before rules with less specific conditions so that the latter can assume that the former will already have been tested and rejected.


Prior to a single test run on each of these samples, neither the lexicon nor the morphological rule system had any exposure to the Brown corpus, nor had either of these word lists been looked at by the experimenter.

the results are a fair evaluation of the expected performance of this system on an unknown domain.


The smoothing paradigm that delivers the best results in TnT is linear interpolation of unigrams, bigrams, and trigrams.

we estimate a trigram probability as follows: /5 are maximum likelihood estimates of the probabilities, and A1 + A2 + A3 = 1, so P again represent probability distributions.


In segmented languages such as English, since words are seemingly separated by white spaces or punctuation marks, tokenization is regarded as a relatively easy task and little attention has been paid to.

each language dependent morphological analyzer has its own strategy for tokenization.


For example, in Figure 5, the results of CPS for "'m in ..." are ..... and "'m," and the results for "New York is ..." are "New" and "New York."

a morphological analyzer with CPSbased dictionary look-up for non-segmented languages can be used for the analysis of segmented languages.


However, the main problem we experienced using the bottom-up strategy was insufficient robustness: because the parser depends on the lower phrasal recognizers, its performance is heavily influenced by their respective performance.

the parser frequently wasn't able to process structurally simple sentences, because they contained, for example, highly complex nominal phrases, as in the following example: "[Die vom Bundesgerichtshof und den Wettbewerbshfitern als Verstofi gegen das Kartellverbot gegeiflelte zentrale TV-Vermarktung] ist g~ngige Praxis."


Furthermore in a sole bottom-up approach some ambiguities -for example relative pronouns -can't be resolved without introducing much underspecification into the intermediate structures.

we propose the following divide-andconquer parsing strategy: In a first phase only the verb groups and the topological structure of a sentence according to the linguistic field the- "[CooraS [sse,,*   ..


The core idea is to call a deep parser only to the separated field elements which contain sequences of simple NPs and PPs (already determined by the shallow parser).

seen the shallow parser is used as an efficient preprocessor for dividing a sentence into syntactically valid smaller units, where the deep parser's task would be to identify the exact constituent structure only on demand.


Because much more external gazetteer information is necessary for the sub-categorization and there is an overlap between male and female name gazetteers, the result from the current MaxEnt module is not sufficiently accurate.

a conservative strategy has been applied.


The rule will for example parse the verbal sequence in the following sentence: (9) Jeg vil *bevarer (berate) rain frihed.

(*I want keep my freedom) of parsing, the system in this case will not attempt to correct the wrong verbal form, but issue the error message "Sequence of two finite verbs".


The data pro-vided by the evaluation reported on in this paper, however, are much too limited to base further development on.

more extensive testing and debugging should also be carried out.


(3) an initial (possibly empty) set of concept classes The pattern set induces a binary partition (a split) on the corpus: on any document, either zero or more than zero patterns will match.

the universe of documents, U, is partitioned into the relevant sub-corpus, R, vs. the non-relevant sub-corpus, R = U -R, with respect to the given pattern set.


The results which follow use this measure.

in the formulas above, R is not simply the count of the relevant documents, but is rather their cumulative relevance.


According to this measure, the system gets a score for each document based on the relevance which it assigned to the document.

if the system .assigned relevance of X percent to a relevant document, it only received X   Figure 3: Results on the MUC corpus percent on the recall score for classifying that document correctly.


Threshold: So that no bias would be unintentionally introduced, we presented subjects with all terms output by each method, up to a specified cut-off poin-However, using lists of equal length for each method would have necessitated either omitting HSs and KWs or changing the definition of TTs.

we made the following decisions: • For TTs, we included all identified terms; • For HSs, we included all terms whose head occurred more than once in the document; .


For example, PP attachment is a difficult problem in parsing and it is not rare that a PP is wrongly attached.

we take this into account when marking the obligatory components using subcategorization knowledge from the lexicon (step 2) -we not only look at the PPs that are attached to a verb phrase, but also PPs that are next to the verb phrase but not attached, in case it is part of the verb phrase.


Within each of the regions, we use a statistical bigram language model, and emit exactly one word upon entering each state.

the number of states in each of the name-class regions is equal to the vocabulary size.


It should be pointed out that the paired t-test takes into consideration the number of subjects.

these findings at these significance levels are particularly strong.


Thus, the results cannot simply be attributed to the misuse of a hierarchical tool.

second, we point out that this GUI was developed as a product, and that many military systems use very similar user interface tools for the same purposes (selecting units)/ these results may have substantial practical impact for users performing this task.


If a clause combination fails, the sp-tree is discarded (for example, if we try to create a relative clause of a structure which already contains a period).

the DSyntS for the entire turn is associated with the root node.


Rankboost, like other machine learning programs of the boosting family, can handle a very large number of features.

instead of carefully choosing a small number of features by hand which may be useful, we generated a very large number of features and let Rank-Boost choose the relevant ones.


We then assume that the words in the name are independent of one another given their label.

we get the following equation: Here w(i) is the ith word from n assigned the label l in l and N (l) is a random variable whose value is the number of words in the name with label l. To put this slightly differently, we first guess the number of words with each label l according to the distribution p(N (l) = n(l)).


For R = f , as we noted earlier, we want to claim that the new name is a member of the same family as that of the earlier name.

as we said earlier, we get "credit" for the repeated family name.


The adverbial function of the adjective difficult is expressed only periphrastically (i.e., in a difficult manner, with difficulty).

the frequency f (difficult, v) was estimated only on the basis of infinitival constructions (see (17)).


First, related senses of a word often exist simultaneously in a discourse (for example the QUANTITY and PROCESS meanings of \increase" above).

systematic polysemy can be e ectively used in WSD (and WSD evaluation) to accept multiple or alternative sense tags (Buitelaar, personal communication).


Third, by explicitly representing (inter-)relations between senses, a lexicon based on systematic polysemy can facilitate semantic inferences.

it is useful in knowledge-intensive NLP tasks such as discourse analysis, IE and MT.


In (Li and Abe, 1998), the tree-cut technique was applied to the problem of acquiring general-ized case frame patterns from a corpus.

each node/word in the tree received as its value the number of instances where the word occurred as a case role (subject, object etc.)


The MDL is a principle of data compression in Information Theory which states that, for a given dataset, the best model is the one which requires the minimum length (often measured in bits) to encode the model (the model description length) and the data (the data description length) (Rissanen, 1978).

the MDL principle captures the trade-o between the simplicity of a model, which is measured by t h e n umber of clusters in a tree-cut, and the goodness of t to the data, which is measured by the estimation accuracy of the probability distribution.


However, our lexicon di ers from CORELEX in that CORELEX looks at all senses of a w ord and groups words that have the same sense distribution pattern, whereas our lexicon groups \drop", \circle", \intersection", \dig", \crossing", \bull's eye" ARTIFACT-GROUP STRUCTURE, PEOPLE] \house", \convent", \market", \center" ARTIFACT-SUBSTANCE FABRIC, CHEMICAL COMPOUND] \acetate", \nylon", \acrylic", \polyester" COMMUNICATION-PERSON VOICE, SINGER] \soprano", \alto", \tenor", \baritone" WRITING, RELIGIOUS PERSON] \John", \Matthew", \Jonah", \Joshua", \Jeremiah" word senses that have the same systematic relation.

our lexicon represents systematic polysemy a t a n e r l e v el than CORELEX, by pinpointing related senses within each w ord.


The result showed that the P-values were 4.17 and 2.65 for nouns and verbs respectively, which w ere both statistically signi cant.

the null hypothesis was rejected, and we concluded that there was a signi cant increase in by using our lexicon.


Yet using this approach, there may be against the human-labeled CELEX lexicon in all circumfixes whose endings will be overlooked in three languages and compare our results to those the search for suffixes unless we first remove all that the Goldsmith and Schone/Jurafsky algorithms candidate prefixes.

we build a lexicon would have obtained on our same data.


Obviously the context space is huge and even in very large training corpora most contexts never occur, which prohibits a reliable probability estimation.

the context space needs to be mapped to a much smaller space, such that only the essential information is retained.


Now the expected frequency of a transition o ∈ {s, p, a} from q to q in a full parse of S is Since all full parses terminate in q F , the final state, ν(q F ) = µ(q F ) = Pr(S).

17is computable as The expected frequencies required for the reestimation of the conditional distributions are then obtained by summing (18) over the state attributes from which the required distribution is independent.


Having the possibility to choose different parameterizations in separate PROJECT and ATTACH submodels did not lower perplexity and increased execution time.

we always used combined PROJECT/ATTACH submodels in further experiments.


We are not aware of another system generating sentence-level paraphrases.

we used as a baseline a simple paraphrasing system that just replaces words with one of their randomly-chosen WordNet synonyms (using the most frequent sense of the word that WordNet listed synonyms for).


Because of strategic differences in question analysis and passage retrieval, our two answering agents often retrieve different passages for the same question.

we perform passage-level combination to make a wider variety of passages available to the answer selection component, as shown in Figure 2.


On the TREC 10 test set, the statistical agent introduced correct answer types in 6 cases and incorrect answer types in 9 cases.

in some cases the question-level combination algorithm improved system performance (comparing A and Q+A) and in others it degraded performance (comparing P and Q+P).


On the other hand, on the TREC 11 test set, the statistical agent introduced correct and incorrect answer types in 15 and 6 cases, respectively.

in most cases performance improved when the question-level combination algorithm was invoked.


After all of the information has been entered, the system confirms its proposed spellings for the names, and, if verified by the user, automatically launches a system update that enters this new information into both the speech recognition component and the natural language (NL) grammar.

the next time the user calls the system, they will be able to log on by speaking their name and password.


We have selected a test set that combines utterances from a preliminary ORION data collection during new user enrollment and previous utterances collected from the JUPITER system (Zue et al., 2000), where at the beginning of each phone call, users are asked to speak and spell their names in a single utterance.

far, 80% of the test set comes from JUPITER data, in which users mostly provided first names.


These clarification dialogues were however presented to the question answering systems already classified and hence systems did not need to recognize that clarification was actually taking place.

systems that simply looked for an answer in the subset of documents retrieved for the first question in a series performed well without any understanding of the fact that the questions constituted a coherent series.


For transducers, where (K, ⊗) = (∆ * , concat), the maximum left factor of F q is the longest common prefix of the strings in range(F q ).

3 we had range(F 3 ) = {wwyz, wwzzz} above with longest common prefix ww.


The probability of the value of £ % depends on the length ' of the original English string.

each foreign word is linked, by virtue of the derivation history, to either nothing (the imaginary NULL word), or exactly one word of the English source sentence.


If the translation probability is zero (which is true most of the time), the resulting alignment probability will be zero.


We assume a high correlation between input length and the hypothesis length.

The original algorithm pursues a highly inefficient search strategy.


With restricted swapping, S has a theoretical complexity of decoding, but the contribution of the JOIN operation to overall time consumption is so small that it can be ignored for all practical purposes.

the average complexity of in practice is ¢ ¡ ¤ £ § , and the total complexity of decoding, which combines up to two CHANGE operations or one CHANGE operation and one INSERT operation, has a practical complexity of .


We assume that improvements are, on average, evenly distributed over the input text.

we can expect the number of places where improvements can be applied to grow with the input length at the same rate as the number of improvements.